
"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_a								=						logging.get_logger(__name__)



def  lowerCamelCase__						(     __snake_case,	__snake_case=False      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    []

					# fmt: off
					# stem:
					rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')      )
					rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')      )

					rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')      )
					rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')      )

					# backbone
					rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')      )
					rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')      )
					rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')      )

					for stage_idx in range(len(config.backbone_config.depths      )      ):
										for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
															rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''')      )
															rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''')      )
															rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''')      )
															rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''')      )
															rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''')      )
															rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''')      )
															rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''')      )
															rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''')      )
															rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''')      )

										rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''')      )
										rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''')      )
										rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''')      )

					# transformer encoder
					for i in range(config.num_hidden_layers      ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')      )
										rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')      )
										rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')      )
										rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')      )
										rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')      )
										rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')      )
										rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')      )
										rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')      )
										rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')      )
										rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')      )

					if base_model:
										# layernorm + pooler
										rename_keys.extend(
										    [
										        ('''norm.weight''', '''layernorm.weight'''),
										        ('''norm.bias''', '''layernorm.bias'''),
										        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
										        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
										    ]      )

										# if just the base model, we should remove "vit" from all keys that start with "vit"
										_UpperCamelCase									=    [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''      ) else pair for pair in rename_keys]
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ('''norm.weight''', '''vit.layernorm.weight'''),
										        ('''norm.bias''', '''vit.layernorm.bias'''),
										        ('''head.weight''', '''classifier.weight'''),
										        ('''head.bias''', '''classifier.bias'''),
										    ]      )
					# fmt: on

					return rename_keys



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case=False      )				->						Optional[int]:


					"""simple docstring"""





					for i in range(config.num_hidden_layers      ):
										if base_model:
															_UpperCamelCase									=    ''''''
										else:
															_UpperCamelCase									=    '''vit.'''
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										_UpperCamelCase									=    state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''      )
										_UpperCamelCase									=    state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''      )
										# next, add query, keys and values (in that order) to the state dict
										_UpperCamelCase									=    in_proj_weight[
										    : config.hidden_size, :
										]
										_UpperCamelCase									=    in_proj_bias[: config.hidden_size]
										_UpperCamelCase									=    in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										_UpperCamelCase									=    in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										_UpperCamelCase									=    in_proj_weight[
										    -config.hidden_size :, :
										]
										_UpperCamelCase									=    in_proj_bias[-config.hidden_size :]



def  lowerCamelCase__						(     __snake_case      )				->						List[str]:


					"""simple docstring"""





					_UpperCamelCase									=    ['''head.weight''', '''head.bias''']
					for k in ignore_keys:
										state_dict.pop(_lowerCamelCase,	_lowerCamelCase      )



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase									=    dct.pop(_lowerCamelCase      )
					_UpperCamelCase									=    val



def  lowerCamelCase__						(     )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase									=    Image.open(requests.get(_lowerCamelCase,	stream=_lowerCamelCase      ).raw      )
					return im



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case=False      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    BitConfig(
					    global_padding='''same''',	layer_type='''bottleneck''',	depths=(3, 4, 9),	out_features=['''stage3'''],	embedding_dynamic_padding=_lowerCamelCase,	)
					_UpperCamelCase									=    ViTHybridConfig(backbone_config=_lowerCamelCase,	image_size=3_84,	num_labels=10_00      )
					_UpperCamelCase									=    False

					# load original model from timm
					_UpperCamelCase									=    timm.create_model(_lowerCamelCase,	pretrained=_lowerCamelCase      )
					timm_model.eval()

					# load state_dict of original model, remove and rename some keys
					_UpperCamelCase									=    timm_model.state_dict()
					if base_model:
										remove_classification_head_(_lowerCamelCase      )
					_UpperCamelCase									=    create_rename_keys(_lowerCamelCase,	_lowerCamelCase      )
					for src, dest in rename_keys:
										rename_key(_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase      )
					read_in_q_k_v(_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase      )

					_UpperCamelCase									=    '''huggingface/label-files'''
					_UpperCamelCase									=    '''imagenet-1k-id2label.json'''
					_UpperCamelCase									=    json.load(open(hf_hub_download(_lowerCamelCase,	_lowerCamelCase,	repo_type='''dataset'''      ),	'''r'''      )      )
					_UpperCamelCase									=    {int(_lowerCamelCase      ): v for k, v in idalabel.items()}
					_UpperCamelCase									=    idalabel
					_UpperCamelCase									=    {v: k for k, v in idalabel.items()}

					# load HuggingFace model
					if vit_name[-5:] == "in21k":
										_UpperCamelCase									=    ViTHybridModel(_lowerCamelCase      ).eval()
					else:
										_UpperCamelCase									=    ViTHybridForImageClassification(_lowerCamelCase      ).eval()
					model.load_state_dict(_lowerCamelCase      )

					# create image processor
					_UpperCamelCase									=    create_transform(**resolve_data_config({},	model=_lowerCamelCase      )      )
					_UpperCamelCase									=    transform.transforms

					_UpperCamelCase									=    {
					    '''bilinear''': PILImageResampling.BILINEAR,
					    '''bicubic''': PILImageResampling.BICUBIC,
					    '''nearest''': PILImageResampling.NEAREST,
					}

					_UpperCamelCase									=    ViTHybridImageProcessor(
					    do_resize=_lowerCamelCase,	size={'''shortest_edge''': timm_transforms[0].size},	resample=pillow_resamplings[timm_transforms[0].interpolation.value],	do_center_crop=_lowerCamelCase,	crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]},	do_normalize=_lowerCamelCase,	image_mean=timm_transforms[-1].mean.tolist(),	image_std=timm_transforms[-1].std.tolist(),	)

					_UpperCamelCase									=    prepare_img()
					_UpperCamelCase									=    transform(_lowerCamelCase      ).unsqueeze(0      )
					_UpperCamelCase									=    processor(_lowerCamelCase,	return_tensors='''pt'''      ).pixel_values

					# verify pixel values
					assert torch.allclose(_lowerCamelCase,	_lowerCamelCase      )

					# verify logits
					with torch.no_grad():
										_UpperCamelCase									=    model(_lowerCamelCase      )
										_UpperCamelCase									=    outputs.logits

					print('''Predicted class:''',	logits.argmax(-1      ).item()      )
					if base_model:
										_UpperCamelCase									=    timm_model.forward_features(_lowerCamelCase      )
										assert timm_pooled_output.shape == outputs.pooler_output.shape
										assert torch.allclose(_lowerCamelCase,	outputs.pooler_output,	atol=1e-3      )
					else:
										_UpperCamelCase									=    timm_model(_lowerCamelCase      )
										assert timm_logits.shape == outputs.logits.shape
										assert torch.allclose(_lowerCamelCase,	outputs.logits,	atol=1e-3      )
					print('''Looks ok!'''      )

					if pytorch_dump_folder_path is not None:
										Path(_lowerCamelCase      ).mkdir(exist_ok=_lowerCamelCase      )
										print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''      )
										model.save_pretrained(_lowerCamelCase      )
										print(F'''Saving processor to {pytorch_dump_folder_path}'''      )
										processor.save_pretrained(_lowerCamelCase      )

					if push_to_hub:
										print(F'''Pushing model and processor to the hub {vit_name}'''      )
										model.push_to_hub(F'''ybelkada/{vit_name}'''      )
										processor.push_to_hub(F'''ybelkada/{vit_name}'''      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--vit_name""",
				    default="""vit_base_r50_s16_384""",
				    type=str,
				    help="""Name of the hybrid ViT timm model you\'d like to convert.""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
				)
				parser.add_argument(
				    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
				)

				_a								=						parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)














"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[Any]          =		{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		['''PLBartTokenizer''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		[
      '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''PLBartForCausalLM''',
      '''PLBartForConditionalGeneration''',
      '''PLBartForSequenceClassification''',
      '''PLBartModel''',
      '''PLBartPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_plbart import PLBartTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_plbart import (
        PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        PLBartForCausalLM,
        PLBartForConditionalGeneration,
        PLBartForSequenceClassification,
        PLBartModel,
        PLBartPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)




